The uncertainty of crop yield projections is reduced by improved temperature response functions

Enli Wang; Pierre Martre; Zhigan Zhao; Frank Ewert; Andrea Maiorano; Reimund P. Rötter; Bruce A. Kimball; Michael J. Ottman; Gerard W. Wall; Jeffrey W. White; Matthew P. Reynolds; Phillip D. Alderman; Pramod K. Aggarwal; Jakarat Anothai; Bruno Basso; Christian Biernath; Davide Cammarano; Andrew J. Challinor; Giacomo de Sanctis; Jordi Doltra; Benjamin Dumont; Elias Fereres; Margarita Garcia-Vila; Sebastian Gayler; Gerrit Hoogenboom; Leslie A. Hunt; Roberto C. Izaurralde; Mohamed Jabloun; Curtis D. Jones; Kurt-Christian Kersebaum; Ann-Kristin Koehler; Lei Liu; Christoph Müller; Soora Kumar Naresh; Claas Nendel; Garry O'Leary; Jørgen E. Olesen; Taru Palosuo; Eckart Priesack; Ehsan Eyshi Rezaei; Dominique Ripoche; Alex C Ruane; Mikhail A. Semenov; Iurii Shcherbak; Claudio O. Stöckle; Pierre Stratonovitch; Thilo Streck; Iwan Supit; Fulu Tao; Peter Thorburn; Katharina Waha; Daniel Wallach; Zhimin Wang; Joost Wolf; Yan Zhu; Senthold Asseng

doi:10.1038/s41477-017-0032-6

Journal article

The uncertainty of crop yield projections is reduced by improved temperature response functions

Enli Wang, Pierre Martre, Zhigan Zhao, Frank Ewert, Andrea Maiorano, Reimund P. Rötter, Bruce A. Kimball, Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, …

Nature Plants, Vol.3(10), pp.833-833

2017

DOI: https://doi.org/10.1038/s41477-017-0032-6

PMID: 28955035

Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

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Title: The uncertainty of crop yield projections is reduced by improved temperature response functions
Creators - without role: Enli Wang - Agriculture and Food
Pierre Martre - Institut Agro Montpellier
Zhigan Zhao - Commonwealth Scientific and Industrial Research Organisation
Frank Ewert - University of Bonn
Andrea Maiorano - Laboratoire d'Écophysiologie Moléculaire des Plantes sous Stress Environnementaux
Reimund P. Rötter - University of Göttingen
Bruce A. Kimball - U.S. Arid Land Agricultural Research Center
Michael J. Ottman - University of Arizona
Gerard W. Wall - Agricultural Research Service
Jeffrey W. White - U.S. Arid Land Agricultural Research Center
Matthew P. Reynolds - Centro Internacional de Mejoramiento de Maíz Y Trigo
Phillip D. Alderman - Centro Internacional de Mejoramiento de Maíz Y Trigo
Pramod K. Aggarwal - Borlaug Institute for South Asia
Jakarat Anothai - Washington State University
Bruno Basso - Michigan State University
Christian Biernath - Helmholtz Zentrum München
Davide Cammarano - University of Florida
Andrew J. Challinor - University of Leeds
Giacomo de Sanctis - European Food Safety Authority
Jordi Doltra - Catabrian Agricultural Research and Training Center
Benjamin Dumont - Michigan State University
Elias Fereres - University of Córdoba
Margarita Garcia-Vila - University of Córdoba
Sebastian Gayler - University of Hohenheim
Gerrit Hoogenboom - Washington State University
Leslie A. Hunt - University of Guelph
Roberto C. Izaurralde - University of Maryland, College Park
Mohamed Jabloun - Aarhus University
Curtis D. Jones - University of Maryland, College Park
Kurt-Christian Kersebaum - Leibniz Centre for Agricultural Landscape Research
Ann-Kristin Koehler - University of Leeds
Lei Liu - Jiangsu Collaborative Innovation Center for Modern Crop Production
Christoph Müller - Potsdam Institute for Climate Impact Research
Soora Kumar Naresh - Centre for Environment Science and Climate Resilient Agriculture
Claas Nendel - Leibniz Centre for Agricultural Landscape Research
Garry O'Leary - Landscape and Water Sciences
Jørgen E. Olesen - Department of Agroecology
Taru Palosuo - Natural Resources Institute Finland
Eckart Priesack - Helmholtz Zentrum München
Ehsan Eyshi Rezaei - University of Bonn
Dominique Ripoche - Agroclim
Alex C Ruane - Goddard Institute for Space Studies
Mikhail A. Semenov - Rothamsted Research
Iurii Shcherbak - Michigan State University
Claudio O. Stöckle - Washington State University
Pierre Stratonovitch - Rothamsted Research
Thilo Streck - University of Hohenheim
Iwan Supit - Wageningen University & Research
Fulu Tao - Natural Resources Institute Finland
Peter Thorburn - Commonwealth Scientific and Industrial Research Organisation
Katharina Waha - Potsdam Institute for Climate Impact Research
Daniel Wallach - AGroecologies, Innovations & Ruralities
Zhimin Wang - China Agricultural University
Joost Wolf - Wageningen University & Research
Yan Zhu - Nanjing Agricultural University
Senthold Asseng - University of Florida
Publication Details: Nature Plants, Vol.3(10), pp.833-833
Identifiers: 9964503609311
Academic Unit: Université de Montpellier
Language: English
Resource Type: Journal article
Local Fields: hal-02629102